Necessary Infrastructure to Benefit from Machine Learning

As with so many other of insurers’ initiatives/goals, possessinga modern, innovative policy administration system (PAS) is absolutely essential. To put it bluntly, decades-old legacy systems are not going to cut it when it comes to machine learning/cognitive computing. It’s difficult enough to capture and process millions of data points today. It will be impossible for systems and processes to reap maximum benefit from the newly gleaned information and improve meaningfully without a 360-degree view of insurance customers and complete visibility. If legacy PAS don’t integrate easily with surrounding solutions and systems, information will end up siloed and machine learning efforts are doomed to fail.

If they haven’t already, insurers should make the leap to pre-integrated, open digital networks that can talk to each other, eliminating integration efforts between the front- and back-end, and facilitating an end-to-end experience to simplify the customer engagement process.

Carriers can also benefit from insurance software vendors’ relationships. An experienced and proven partner can act as a trusted advisor to insurers, bringing best practices that have saved other leading insurers time and money, and recommend machine-learning specialist partners.

An effective advanced analytics solution is critical for machine learning. Insurers are already drowning in data from IoT/wearables, customer engagement platforms, social media, etc. They are trying to use this data to formulate their companies’ strategies, decide which new propositions to bring to market and obtain crucial business intelligence. Without effective analytics, insurers are reactive and cannot effectively prepare for new trends. They don’t have the data points necessary for machine learning. An effective system should empower business users to easily and rapidly draw business conclusions and insights from raw data, via self-service analytics. This will help fan the flames of the machine learning inferno.

But advanced analytics aren’t enough. Rather than trying to apply machine learning to a specific process or problem, insurers should strive to establish a fully digital business with machine learning integrated throughout. The ideal scenario is an all-encompassing digital approach that features integrated components functioning at their highest levels and complementing each other, integrated with important digital services such as machine learning, as well as robo-advice, Internet of Things-related applications, etc.

And as noted earlier, machine learning shouldn’t be harnessed solely to increase efficiency or reduce margins. To truly succeed in 2018 and beyond, insurers would be wise to ensure that they transform into service providers who impact and influence the lifestyles of their customers and provide a unique experience. Customer portals and engagement platforms have an important role to play here. Portals offer millennials and other digitally-savvy insureds the online presence they require, while customer engagement platforms that support wearables are valuable for generating lots of potential touch-points. They keep the insured engaged with his/her provider, but the interaction needs to progress beyond that for maximum effectiveness.

Machine learning will play an important role for insurers sooner than many think, so the time to start preparing is now. My NEW white paper: Learning about Machine Learning, will give you the full picture on how to do so…

Gil Maletski

Gil Maletski is the chief technology officer for the general insurance division at Sapiens. He possesses strong software architecture and design capabilities, with deep managerial, business and technical understanding.